A microfluidic method for selecting chemotactic stem cells
Bibliographic record
Abstract
Stem cells hold great promise for treating various degenerative diseases. However, the outcomes of preclinical and clinical cell therapy studies are still not close to our expectation. The unsatisfactory outcomes of cell therapy are at least partially due to: 1) insufficient homing of implanted stem cells into target organs and 2) use of heterogeneous cell populations for cell therapy. Therefore, there is a need to develop effective guiding technique for stem cells to migrate to the target organs and to isolate effective stem cell populations. In this project, I developed a microfluidics-based method for selecting chemotactic adipose-derived stem cells (ASCs) to epidermal growth factor (EGF). This method integrates cell patterning, chemotaxis and cell extraction on a single microfluidic device. Post-extraction analysis confirmed the higher chemotactic ability of the extracted cells to EGF. The extracted chemotactic ASCs shows up-regulated surface expression of EGF receptor and its downstream signaling event upon EGF stimulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".